# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import warnings

import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
                         wrap_fp16_model)

import mmdet
from mmdet3d.apis import single_gpu_test
from mmdet3d.datasets import build_dataloader, build_dataset
from mmdet3d.models import build_model
from mmdet.apis import multi_gpu_test, set_random_seed
from mmdet.datasets import replace_ImageToTensor

if mmdet.__version__ > '2.23.0':
    # If mmdet version > 2.23.0, setup_multi_processes would be imported and
    # used from mmdet instead of mmdet3d.
    from mmdet.utils import setup_multi_processes
else:
    from mmdet3d.utils import setup_multi_processes

try:
    # If mmdet version > 2.23.0, compat_cfg would be imported and
    # used from mmdet instead of mmdet3d.
    from mmdet.utils import compat_cfg
except ImportError:
    from mmdet3d.utils import compat_cfg


def load_params_from_file(self, filename, logger, to_cpu=False):
    if not os.path.isfile(filename):
        raise FileNotFoundError

    print('==> Loading parameters from checkpoint %s to %s' % (filename, 'CPU' if to_cpu else 'GPU'))
    loc_type = torch.device('cpu') if to_cpu else None
    checkpoint = torch.load(filename, map_location=loc_type)
    model_state_disk = checkpoint['state_dict']
    # for i,k in model_state_disk.items():
    #     print(i,k.shape)
    # 'lolo_vfe.vfe.0.weight', 'lolo_vfe.vfe.1.weight', 'lolo_vfe.vfe.1.bias', 'lolo_vfe.vfe.1.running_mean', 'lolo_vfe.vfe.1.running_var', 'lolo_vfe.vfe.1.num_batches_tracked'
    if 'version' in checkpoint:
        print('==> Checkpoint trained from version: %s' % checkpoint['version'])

    state_dict = self.state_dict()   #add

    update_model_state = {}
    for key, val in model_state_disk.items():
        if key in state_dict and state_dict[key].shape == model_state_disk[key].shape:
            update_model_state[key] = val
            # logger.info('Update weight %s: %s' % (key, str(val.shape)))
    # add unmatched dict

    update_model_state['lolo_vfe.vfe.0.weight'] = model_state_disk['pts_voxel_encoder.pfn_layers.0.linear.weight'].unsqueeze(-1).unsqueeze(-1)     #(64, 4, 1, 1)
    update_model_state['lolo_vfe.vfe.1.weight'] = model_state_disk['pts_voxel_encoder.pfn_layers.0.norm.weight']
    update_model_state['lolo_vfe.vfe.1.bias'] = model_state_disk['pts_voxel_encoder.pfn_layers.0.norm.bias']
    update_model_state['lolo_vfe.vfe.1.running_mean'] = model_state_disk['pts_voxel_encoder.pfn_layers.0.norm.running_mean']
    update_model_state['lolo_vfe.vfe.1.running_var'] = model_state_disk['pts_voxel_encoder.pfn_layers.0.norm.running_var']

    #
    # state_dict = self.state_dict()
    state_dict.update(update_model_state)
    self.load_state_dict(state_dict)

    for key in state_dict:
        if key not in update_model_state:
            print('Not updated weight %s: %s' % (key, str(state_dict[key].shape)))   # backbone_2d.layers.1.1.conv2.weight

    print('==> Done (loaded %d/%d)' % (len(update_model_state), len(self.state_dict())))
    return self

def parse_args():
    parser = argparse.ArgumentParser(
        description='MMDet test (and eval) a model')
    parser.add_argument('config', help='test config file path')
    parser.add_argument('checkpoint', help='checkpoint file')
    parser.add_argument('--out', help='output result file in pickle format')
    parser.add_argument(
        '--fuse-conv-bn',
        action='store_true',
        help='Whether to fuse conv and bn, this will slightly increase'
        'the inference speed')
    parser.add_argument(
        '--gpu-ids',
        type=int,
        nargs='+',
        help='(Deprecated, please use --gpu-id) ids of gpus to use '
        '(only applicable to non-distributed training)')
    parser.add_argument(
        '--gpu-id',
        type=int,
        default=0,
        help='id of gpu to use '
        '(only applicable to non-distributed testing)')
    parser.add_argument(
        '--format-only',
        action='store_true',
        help='Format the output results without perform evaluation. It is'
        'useful when you want to format the result to a specific format and '
        'submit it to the test server')
    parser.add_argument(
        '--onnx_file',
        type=str,
        help='')
    parser.add_argument('--show', action='store_true', help='show results')
    parser.add_argument(
        '--show-dir', help='directory where results will be saved')
    parser.add_argument(
        '--gpu-collect',
        action='store_true',
        help='whether to use gpu to collect results.')
    parser.add_argument(
        '--no-aavt',
        action='store_true',
        help='Do not align after view transformer.')
    parser.add_argument(
        '--tmpdir',
        help='tmp directory used for collecting results from multiple '
        'workers, available when gpu-collect is not specified')
    parser.add_argument('--seed', type=int, default=0, help='random seed')
    parser.add_argument(
        '--deterministic',
        action='store_true',
        help='whether to set deterministic options for CUDNN backend.')
    parser.add_argument(
        '--cfg-options',
        nargs='+',
        action=DictAction,
        help='override some settings in the used config, the key-value pair '
        'in xxx=yyy format will be merged into config file. If the value to '
        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
        'Note that the quotation marks are necessary and that no white space '
        'is allowed.')
    parser.add_argument(
        '--options',
        nargs='+',
        action=DictAction,
        help='custom options for evaluation, the key-value pair in xxx=yyy '
        'format will be kwargs for dataset.evaluate() function (deprecate), '
        'change to --eval-options instead.')
    parser.add_argument(
        '--eval-options',
        nargs='+',
        action=DictAction,
        help='custom options for evaluation, the key-value pair in xxx=yyy '
        'format will be kwargs for dataset.evaluate() function')
    parser.add_argument(
        '--launcher',
        choices=['none', 'pytorch', 'slurm', 'mpi'],
        default='none',
        help='job launcher')
    parser.add_argument('--local_rank', type=int, default=0)
    args = parser.parse_args()
    if 'LOCAL_RANK' not in os.environ:
        os.environ['LOCAL_RANK'] = str(args.local_rank)

    if args.options and args.eval_options:
        raise ValueError(
            '--options and --eval-options cannot be both specified, '
            '--options is deprecated in favor of --eval-options')
    if args.options:
        warnings.warn('--options is deprecated in favor of --eval-options')
        args.eval_options = args.options
    return args


def main():
    args = parse_args()

    # assert args.out or args.eval or args.format_only or args.show \
    #     or args.show_dir, \
    #     ('Please specify at least one operation (save/eval/format/show the '
    #      'results / save the results) with the argument "--out", "--eval"'
    #      ', "--format-only", "--show" or "--show-dir"')

    # if args.eval and args.format_only:
    #     raise ValueError('--eval and --format_only cannot be both specified')

    # if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
    #     raise ValueError('The output file must be a pkl file.')

    cfg = Config.fromfile(args.config)
    if args.cfg_options is not None:
        cfg.merge_from_dict(args.cfg_options)

    cfg = compat_cfg(cfg)

    # set multi-process settings
    setup_multi_processes(cfg)

    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True

    cfg.model.pretrained = None

    if args.gpu_ids is not None:
        cfg.gpu_ids = args.gpu_ids[0:1]
        warnings.warn('`--gpu-ids` is deprecated, please use `--gpu-id`. '
                      'Because we only support single GPU mode in '
                      'non-distributed testing. Use the first GPU '
                      'in `gpu_ids` now.')
    else:
        cfg.gpu_ids = [args.gpu_id]

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    test_dataloader_default_args = dict(
        samples_per_gpu=1, workers_per_gpu=2, dist=distributed, shuffle=False)

    # in case the test dataset is concatenated
    if isinstance(cfg.data.test, dict):
        cfg.data.test.test_mode = True
        if cfg.data.test_dataloader.get('samples_per_gpu', 1) > 1:
            # Replace 'ImageToTensor' to 'DefaultFormatBundle'
            cfg.data.test.pipeline = replace_ImageToTensor(
                cfg.data.test.pipeline)
    elif isinstance(cfg.data.test, list):
        for ds_cfg in cfg.data.test:
            ds_cfg.test_mode = True
        if cfg.data.test_dataloader.get('samples_per_gpu', 1) > 1:
            for ds_cfg in cfg.data.test:
                ds_cfg.pipeline = replace_ImageToTensor(ds_cfg.pipeline)

    test_loader_cfg = {
        **test_dataloader_default_args,
        **cfg.data.get('test_dataloader', {})
    }

    # set random seeds
    if args.seed is not None:
        set_random_seed(args.seed, deterministic=args.deterministic)

    # build the dataloader
    dataset = build_dataset(cfg.data.test)
    data_loader = build_dataloader(dataset, **test_loader_cfg)

    # build the model and load checkpoint
    # if not args.no_aavt:
    #     if '4D' in cfg.model.type:
    #         cfg.model.align_after_view_transfromation=True
    cfg.model.train_cfg = None
    model = build_model(cfg.model, test_cfg=cfg.get('test_cfg'))
    fp16_cfg = cfg.get('fp16', None)
    if fp16_cfg is not None:
        wrap_fp16_model(model)
    checkpoint = load_params_from_file(model, args.checkpoint, None)
    if args.fuse_conv_bn:
        model = fuse_conv_bn(model)
    # old versions did not save class info in checkpoints, this walkaround is
    # for backward compatibility


    # model = MMDataParallel(model, device_ids=cfg.gpu_ids)
    # outputs = single_gpu_test(model, data_loader, args.show, args.show_dir)
    model.eval()
    dataset = data_loader.dataset
    for i, data in enumerate(data_loader):
        data.pop('img_metas')
        data.pop('points')
        data.pop('gt_maps_3d')
        data['img_inputs'][0] = data['img_inputs'][0][:3]
        data['img_inputs'][0][0] = data['img_inputs'][0][0].squeeze(0)
        data['img_inputs'][0][1] = torch.zeros(1,59,16384)
        data['img_inputs'][0][2] = torch.zeros(1,264,16384)
        lidar_inputs = torch.zeros(1,4,2048,2048)
        pre_pose = torch.zeros(1,2, 128,128)
        pre_feat = torch.zeros(1,256,128,128)
        dummy_input = data['img_inputs'][0]
        dummy_input = dummy_input + [pre_pose] + [pre_feat]
        dummy_input = dummy_input + [lidar_inputs]
        # result = model(**data)
        dummy_output = model(dummy_input)
        onnx_output_path = args.onnx_file
        # for i in range(6):
        comm = cfg['model']['pts_bbox_head']['common_heads']
        head = cfg['model']['pts_bbox_head']['tasks']
        output_names_box = []
        for k, task in enumerate(head):
            for s in comm.keys():
                output_names_box.append(f"box_{k}_{s}")
            output_names_box.append(f"box_{k}_cls")
        
        comm = cfg['model']['map_head']['common_heads']
        head = cfg['model']['map_head']['tasks']
        output_names_map = []
        for k, task in enumerate(head):
            for s in comm.keys():
                output_names_map.append(f"map_{k}_{s}")
            output_names_map.append(f"map_{k}_cls")
        output_names = []
        output_names.extend(output_names_box)
        output_names.extend(output_names_map)
        output_names.append('bev_feat')
        torch.onnx.export(model,          # model being run
        dummy_input,                      # model input (or a tuple for multiple inputs)
        onnx_output_path,                 # where to save the model
        verbose=False,
        export_params=True,               # store the trained parameter weights inside the model file
        opset_version=13,                 # the ONNX version to export the model to
        do_constant_folding=True,         # whether to execute constant folding for optimization
        enable_onnx_checker=False,
        input_names = ['img_input','ring_matrix','ray_matrix','align_matrix','pre_input','lidar_input'],   # the model's input names
        output_names=output_names,
        # dynamic_axes={'modelInput' : {0 : 'batch_size'},    # variable length axes
        #     'modelOutput' : {0 : 'batch_size'}}
    )
        break
if __name__ == '__main__':
    main()
